Why a Top-Down Low Code Strategy is Increasingly Critical to Harness AI

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As an enterprise leader, a “low-code strategy” may not be on the top of your mind yet—but it presents a valuable opportunity to digitally transform your organization. Gartner notes that by 2025 “70% of new applications developed by organizations will use low-code or no-code technologies, up from less than 25% in 2020. The rise of low-code application platforms (LCAPs) is driving the increase of citizen development, and notably the function of business technologists who report outside of IT departments and create technology or analytics capabilities for internal or external business use.”

The relevance of low-code is not limited to software developers or IT teams—rather, it can benefit from top-down business strategy. Low-code adoption is a catalyst for increased organizational agility, faster go-to-market product cycles, better cost-effectiveness, and greater talent and resource management. The right low-code strategy does this with:


The speed of technology evolution dictates organizations to be more agile. Yesterday's tech stack may already be outdated today, but the elegance of low-code is its modularity. Independent components create a plug-and-play environment within an application flow. Therefore, an organization can make small changes rapidly, literally iterating to the market.


Low-code componentizes blocks of code, allowing their reusability, which improves development times compared to traditional coding methods. Rapid assembly of pre-built components into flows, nodes, and templates simplifies software development. Consider the current traditional model, where teams of developers, even in remote locations, have to manage complex sprints to enable integrations between frontend and backend applications, legacy systems, and data silos. Low-code speeds this up 10x, if not much faster.


Code reusability, shorter development cycles, and simplifying workloads all cumulatively reduce software development costs. Furthermore, over the long run, these savings make a significant impact to your budgets when building, upgrading, and integrating new applications.

Talent and resource management

If we consider a macro picture, there are roughly 25 million software developers in the world. In contrast, the global talent for AI engineers is around 300,000, according to Tencent. An organization with limited resources would be hard-challenged to compete for AI talent. Yet at the same time, it would be foolhardy to ignore the importance of AI application development, given that enterprise AI technology is growing at over a 20% CAGR. Low-code brings accessibility to AI development.

Using the same methods of componentizing blocks of codes, pre-built AI/ML models can quickly be customized and deployed for commercial applications. From a more micro point of view, low-code upskills your existing developer team. For example, a web engineer can easily use a low-code platform—with existing AI components—to build AI-powered chatbots/voicebots, product recommenders, knowledge graphs, computer vision applications, and much more. 

Similarly, non-developers can embrace a low-code environment to drag and drop blocks and make enterprise applications, not necessarily just AI ones. These can be frontend web forms, mobile apps, HR/finance databases, etc. Upskilling with low-code in effect maximizes the productivity of not only your developer teams, but also your entire organization.

There is also an implicit but powerful advantage to strongly invest in low-code for your enterprise AI development. There is a “ secret” about relying on external vendors to develop AI applications provided by vendors, whether via SaaS or license models. The intellectual property that comes from developing your use cases—the AI/ML models and algorithms—is not necessarily yours.

Oftentimes, your proprietary data that is needed to build out your AI use cases are training your vendors’ models, which is their IP. Considering the effort required to gather, manage, and process data, not being able to own any of the final assets is a considerable missed opportunity. 

Before assuming that owning your AI/ML IP necessitates recruiting a team of data scientists and AI engineers, low-code offers a very practical strategy shift. While pre-built AI/ML models and open-source libraries can be easily integrated into the environment, these resources can be customized (by your existing developer team as previously established) into your own proprietary IP. 

Training these models with your own data, you can create your own “derived” AI, which you can tailor to your own unique business cases. Keeping your own AI model development in-house ensures the complete stewardship of your valuable data and its privacy, as well as the flexibility to use those models and their algorithms for any other use cases. Again, adopting low-code within your organization affords innumerable benefits for your business strategy.

The general philosophy around digital innovation is to resist inertia, be agile, and adapt to change. As such, digitally forward leaders and their organizations should always have a sense of urgency about technology. When thinking about innovation, today is the day to start.  Tomorrow is too late. Low-code is no exception; the opportunities you embrace with its strategy will pay off the sooner you start.

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